SoroushVahidi/scidocs huggingface dataset:
Bayesian Optimization Algorithm
(2005)

In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and mixes building blocks up to a speci ed order It is independent of the ordering of the variables in the strings rep resenting the solutions Moreover prior in formation about the problem can be incor porated into the algorithm However prior information is not essential Preliminary ex periments show that the BOA outperforms the simple genetic algorithm even on decom posable functions with tight building blocks as a problem size grows
Space-Efficient Online Computation of Quantile Summaries
(2001)

An ∈-approximate quantile summary of a sequence of <i>N</i> elements is a data structure that can answer quantile queries about the sequence to within a precision of ∈<i>N</i>.
We present a new online algorithm for computing∈-approximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirement of <i>&Ogr;</i>(1÷∈ log(∈<i>N</i>)). This improves upon the previous best result of <i>&Ogr;</i>(1÷∈ log<sup>2</sup>(∈<i>N</i>)). Moreover, in contrast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence.
Finally, the actual space bounds obtained on experimental data are significantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.
Load Disaggregation Based on Aided Linear Integer Programming
(2017)

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.
Information flow and cooperative control of vehicle formations
(2004)

We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability.
Enhanced Visibility of MoS 2 , MoSe 2 , WSe 2 and Black-Phosphorus : Making Optical Identification of 2 D Semiconductors Easier
(2015)

We explore the use of Si3N4/Si substrates as a substitute of the standard SiO2/Si substrates employed nowadays to fabricate nanodevices based on 2D materials. We systematically study the visibility of several 2D semiconducting materials that are attracting a great deal of interest in nanoelectronics and optoelectronics: MoS2, MoSe2, WSe2 and black-phosphorus. We find that the use of Si3N4/Si substrates provides an increase of the optical contrast up to a 50%–100% and also the maximum contrast shifts towards wavelength values optimal for human eye detection, making optical identification of 2D semiconductors easier. OPEN ACCESS Electronics 2015, 4 848
Scaling Similarity Joins over Tree-Structured Data
(2015)

Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-ofthe-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.
Design and simulation of a Sierpinski carpet fractal antenna for 5G commercial applications
(2017)

This article presents the design and simulation of a multi-band microstrip patch antenna, using Sierpinski carpet fractal concept. The proposed antenna is designed on a FR-4 substrate with a relative permittivity of 4.4 and excited using a microstrip feed line at the edge of the patch. Sierpinski carpet fractal with iteration-3 is applied to the radiating patch. After the application of fractal, this patch antenna resonates at 14.80 GHz, 17.92 GHz, 23.12 GHz, and 27.92 GHz with impedance bandwidth of 5800 MHz, 3040 MHz, 2960 MHz, and 3840 MHz respectively. With these four bands, this antenna finds application in commercial 5G wireless communications. To illustrate the effectiveness of the fractal concept, four patch antennas are designed independently at those four resonating frequencies and compared with the single fractal antenna in terms of their size and other performance parameters. All the antennas proposed here, are designed using HFSS15 and results are analyzed in terms of return loss, VSWR, gain, and bandwidth.
OpenCV based disease identification of mango leaves
(2016)

This paper aims in classifying and identifying the diseases of mango leaves for Indian agriculture. K-means algorithm is chosen for the disease segmentation, and the disease classification and identification is carried out using the SVM classifier. Disease identification based on analysis of patches or discoloring of leaf will hold good for some of the plant diseases, but some other diseases which will deform the leaf shape cannot be identified based on the same method. In this case leaf shape based disease identification has to be performed. Based on this analysis two topics are addressed in this research paper. (1) Disease identification using the OpenCV libraries (2) Leaf shape based disease identification. Keywordk-means,Principal Component Analysis (PCA), feature extraction, shape detection, disease identification, Elliptic fourier analysis, Support Vector Machine(SVM), Artificial Neural Network (ANN)
Assessing the benefits of computational offloading in mobile-cloud applications
(2015)

This paper presents the results of a formative study conducted to determine the effects of computation offloading in mobile applications by comparing “application performance” (chiefly energy consumption and response time). The study examined two general execution scenarios: (1) computation is performed locally on a mobile device, and (2) when it is offloaded entirely to the cloud. The study also carefully considered the underlying network characteristics as an important factor affecting the performance. More specifically, we refactored two mobile applications to offload their computationally intensive functionality to execute in the cloud. We then profiled these applications under different network conditions, and carefully measured “application performance” in each case. The results were not as conclusive as we had expected. On fast networks, offloading is almost always beneficial. However, on slower networks, the offloading cost-benefit analysis is not as clear cut. The characteristics of the data transferred between the mobile device and the cloud may be a deciding factor in determining whether offloading a computation would improve performance.
A Batteryless Sensor ASIC for Implantable Bio-Impedance Applications
(2016)

The measurement of the biological tissue's electrical impedance is an active research field that has attracted a lot of attention during the last decades. Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications. Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin. This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz. The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm × 1.22 mm and consumes 165 μA from a 1.8 V power supply. The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling. The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs. The proposed ASIC is able to extract complex impedances with around 1 Ω resolution; therefore enabling accurate wireless tissue measurements.
Skeleton-based Adaptive In-between Generation for Hand-drawn Key Frames
(2011)

For improving the efficiency of 2D animation production, this paper presents a method to create in-between frames based on hand-drawn key-frames. The outlines of characters or objects on two key-frames are used as inputs. First, a skeleton linkage of the target object is automatically constructed by rasterizing each of input key-frames and then applying a pixel-based skeleton extraction method. Secondary, a pair of skeleton linkages having corresponding structure between the current key-frame and the next key-frame is constructed by applying the stroke matching algorithm. After these processes, motion transitions between the skeleton linkages are generated based on our simulation model. When the in-between frames are created only in the 2D plane, the outlines at in-between frames can be generated by a 2D deformation. In case that the in-between transitions are containing a rotation around an axis which is no perpendicular to the drawing plane, however, a 3D structure is required. For achieving such in-between transitions, our method constructs a 3D structure by inflating 2D mesh based on the input outlines. Finally, the contours from the view-point for the created 3D structure are projected onto the 2D plane during in-between transitions. In our method, we adopt the Photic Extremum Lines (PEL) to extract the 2D contours from the obtained 3D shape. In this way, we achieve the in-between creation containing spatial rotation such as hand-flipping, which has not been achieved by general ways of in-between creation method.
Memory-Efficient Adaptive Optimization for Large-Scale Learning
(2019)

Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each parameter, thus doubling the memory footprint of the optimizer. In behemoth-size applications, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel, simple, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training very large deep models.
Better Approximation of Betweenness Centrality
(2008)

Centrality indices are used to classify a graph in important and unimportant vertices. A commonly used index is betweenness centrality which is based on shortest paths. For large graphs it is almost impossible to calculate exact betweenness centrality in appropriate time. The best known algorithm today is based on solving n SSSPs and requires O(nm+ n2 log(n)) time. But in most cases, e.g. on a home computer, resources are limited. Current approximation algorithms extrapolate values by solving only k ≪ n SSSPs. Vertices near the source of a SSSP are often overestimated. We introduce improvements which take the distance to the source into account. Euclidean distance between approximation and exact values is reduced by factor 4 with same runtime. Or runtime is 16 times faster with same Euclidean distance in a standard example, the movie actor network. Other real-world networks show similar promising results.
Am I wasting my time organizing email?: a study of email refinding
(2011)

We all spend time every day looking for information in our email, yet we know little about this refinding process. Some users expend considerable preparatory effort creating complex folder structures to promote effective refinding. However modern email clients provide alternative opportunistic methods for access, such as search and threading, that promise to reduce the need to manually prepare. To compare these different refinding strategies, we instrumented a modern email client that supports search, folders, tagging and threading. We carried out a field study of 345 long-term users who conducted over 85,000 refinding actions. Our data support opportunistic access. People who create complex folders indeed rely on these for retrieval, but these preparatory behaviors are inefficient and do not improve retrieval success. In contrast, both search and threading promote more effective finding. We present design implications: current search-based clients ignore scrolling, the most prevalent refinding behavior, and threading approaches need to be extended.
Partially supervised clustering for image segmentation
(1996)

All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tendency to recommend solutions that equalize cluster populations. The semi-supervised c-means algorithms introduced in this paper attempt to overcome these three problems for problem domains where a few data from each class can be labeled. Segmentation of magnetic resonance images is a problem of this type and we use it to illustrate the new algorithm. Our examples show that the semi-supervised approach provides MRI segmentations that are superior to ordinary fuzzy c-means and to the crisp k-nearest neighbor rule and further, that the new method ameliorates (P1)-(P3). Cluster analysis Fuzzy c-means Partial supervision Image segmentation Magnetic resonance images
Design and Implementation of a Network Security Model for Cooperative Network
(2009)

In this paper a design and implementation of a network security model was presented, using routers and firewall. Also this paper was conducted the network security weakness in router and firewall network devices, type of threats and responses to those threats, and the method to prevent the attacks and hackers to access the network. Also this paper provides a checklist to use in evaluating whether a network is adhering to best practices in network security and data confidentiality. The main aim of this research is to protect the network from vulnerabilities, threats, attacks, configuration weaknesses and security policy weaknesses. KeywordsNetwork security, network management, threats, security policy Received December 17, 2008; Accepted March 2 2009
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
(2016)

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.
Partially observable Markov decision processes for spoken dialog systems
(2007)

In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed on three largely disparate techniques: parallel dialog state hypotheses, local use of confidence scores, and automated planning. While in isolation each of these approaches can improve action selection, taken together they currently lack a unified statistical framework that admits global optimization. In this paper we cast a spoken dialog system as a partially observable Markov decision process (POMDP). We show how this formulation unifies and extends existing techniques to form a single principled framework. A number of illustrations are used to show qualitatively the potential benefits of POMDPs compared to existing techniques, and empirical results from dialog simulations are presented which demonstrate significant quantitative gains. Finally, some of the key challenges to advancing this method – in particular scalability – are briefly outlined. 2006 Elsevier Ltd. All rights reserved.
Human balance and posture control during standing and walking
(2004)

The fact that we as humans are bipeds and locomote over the ground with one foot in contact (walking), no feet in contact (running), or both feet in contact (standing) creates a major challenge to our balance control system. Because two-thirds of our body mass is located two-thirds of body height above the ground we are an inherently unstable system unless a control system is continuously acting. The purpose of this review is to introduce the reader to the basic anatomy, mechanics, and neuromuscular control that is seen in the current literature related to balance control. The degeneration of the balance control system in the elderly and in many pathologies has forced researchers and clinicians to understand more about how the system works and how to quantify its status at any point in time. With the increase in our ageing population and with increased life expectancy of our elderly the importance of maintaining mobility is becoming ever more critical. Injuries and loss of life due to falls in the elderly is a very major factor facing them. Table 1 compares the deaths due to falls with the deaths due to motor vehicle accidents (MVA) as a function of age as reported by Statistics Canada in 1991. The startling fact is that the deaths due to falls in the SO+ population is almost as high as the MVA deaths in the accident-prone l5-29-year group. However, when we look at the deaths per 100 000 for these two causes the figures are even more startling. The young 15-29-year-old group had 21.5 MVA deaths per 100 000 while the elderly deaths
Enabling Public Verifiability and Data Dynamics for Storage Security
(2009)

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.
The Effect of Noise on Sofware Engineers' Performance
(2018)

Background: Noise, dened as an unwanted sound, is one of the commonest factors that could aect people’s performance in their daily work activities. e soware engineering research community has marginally investigated the eects of noise on soware engineers’ performance. Aims: We studied if noise aects soware engineers’ performance in: (i) comprehending functional requirements and (ii) xing faults in source code. Method: We conducted two experiments with nal-year undergraduate students in Computer Science. In the rst experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to x faults in Java code. Results: e participants in the second experiment, when exposed to noise, had signicantly worse performance in xing faults in source code. On the other hand, we did not observe any statistically signicant dierence in the rst experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.